An Introduction to Political and Social Data Analysis (With R)

An Introduction to Political and Social Data Analysis (With R) provides students with an accessible overview of practical data analysis while also providing a gentle introduction to R. By starting with statistics first and using just enough R code to generate results, this text helps students focus on learning how to do data analysis while slowly gaining confidence in using R as they progress through the material. This book is structured around learning by doing. Students can follow along in each chapter by reading about statistics and their applications in R, and then running the R code on their own as they work through contemporary political science and social science examples. Author Thomas M. Holbrook patiently explains each step in in the process, avoiding overly complicated jargon and commands. Exercises at the end of chapters feature both conceptual and calculation-based questions so students can check their understanding of data analysis and practice using R. At the end of the semester, students can confidently add skills in data analysis with R to their resumes.

Table of Contents:
Chapter 1: Introduction to Research and Data Political and Social Data Analysis Data Analysis or Statistics? Uses of Data Analysis The Research Process Other Data-Related Issues Causal Language Next Steps Exercises Chapter 2: Using R to Do Data Analysis Accessing R Opening RStudio Understanding Where R (or Any Program) Fits In Time to Use R Some R Terminology Managing Files and Output Next Steps Exercises Chapter 3: Frequencies and Basic Graphs Get Ready Introduction Frequencies Graphing Outcomes Next Steps Exercises Chapter 4: Data Preparation Get Ready Introduction Data Transformations Collapsing and Reordering Categories Combining Variables Save Your Changes Next Steps Exercises Chapter 5: Measures of Central Tendency Get Ready Central Tendency Mode Median The Mean Mean, Median, and the Distribution of Variables Skewness Statistic Adding Legends to Graphs Next Steps Exercises Chapter 6: Measures of Dispersion Get Ready Introduction Measures of Spread Dispersion Around the Mean Dichotomous Variables Dispersion in Categorical Variables? The Standard Deviation and the Normal Curve Calculating Area Under a Normal Curve One Last Thing Next Steps Exercises Chapter 7: Probability Get Ready Probability Theoretical Probabilities Empirical Probabilities The Normal Curve and Probability Next Steps Exercises Chapter 8: Sampling and Inference Get Ready Statistics and Parameters Sampling Error Sampling Distributions Proportions Confidence Intervals Next Steps Exercises Chapter 9: Hypothesis Testing Get Ready The Logic of Hypothesis Testing Direct Hypothesis Tests Proportions T-Distribution Types of Error t-test in R Next Steps Exercises Chapter 10: Hypothesis Testing with Two Groups Get Ready Testing Hypotheses About Two Means Hypothesis Testing With Two Means Difference in Proportions Plotting Mean Differences What’s Next? Exercises Chapter 11: Hypothesis Testing With Multiple Groups (ANOVA) Get Ready Internet Access as an Indicator of Development The Relationship Between Wealth and Internet Access Analysis of Variance Anova in R Effect Size Connecting the t-score and F-ratio Next Steps Exercises Chapter 12: Hypothesis Testing with Non-Numeric Variables (Crosstabs) Get Ready Crosstabs Sampling Error Hypothesis Testing With Crosstabs (Chi-square) Get Ready Directional Patterns in Crosstabs Limitations of Chi-square Next Steps Exercises Chapter 13: Measures of Association Get Ready Going Beyond Chi-squared Measures of Association for Crosstabs Ordinal Measures of Association Revisiting the Gender Gap in Abortion Attitudes Next Steps Exercises Chapter 14: Correlation and Scatterplots Get Ready Relationships Between Numeric Variables Scatterplots Pearson’s r Variation in Strength of Relationships Proportional Reduction in Error Correlation and Scatterplot Matrices Overlapping Explanations Next Steps Exercises Chapter 15: Simple Regression Get Ready Linear Relationships Ordinary Least Squares Regression How Well Does the Model Fit the Data? Proportional Reduction in Error Getting Regression Results in R Understanding the Constant Organizing the Regression Output Revisiting Life Expectancy Important Caveat Adding Regression Information to Scatterplots Next Steps Exercises Chapter 16: Multiple Regression Get Ready Multiple Regression Model Accuracy Predicted Outcomes Revisiting Presidential Votes in the States Next Steps Exercises Chapter 17: Advanced Regression Topics Get Ready Incorporating Access to Health Care Multicollinearity Checking on Linearity Which Variables Have the Greatest Impact? Statistics Versus Substance Next Steps Exercises Chapter 18: Regression Assumptions Get Ready Regression Assumptions Next Steps Exercises Appendix A: Codebooks Appendix B: Quarto Tutorial Appendix C: Hidden R Code Endnotes Index

About the Author :
Thomas M. Holbrook is Emeritus Professor at the University of Wisconsin-Milwaukee, where he was a Distinguished Professor and the Wilder Crane Professor of Government in the political science department. He is a former editor of American Politics Research and the author of Do Campaigns Matter (Sage, 1996), Altered States (Oxford, 2016), and dozens of articles on various aspects of voting behavior and elections in the United States, most recently focusing on local politics. Professor Holbrook has taught undergraduate courses on data analysis and survey research for the past three decades and has integrated R into his data analysis courses for the past several years.

Review :
Clarity in communication is absolutely essentialin introductory methodology and data science courses. Holbrook′s way with words makes complicated statistical and computational language easy to understand and instills confidence in students. The book introduces many useful concepts without getting too bogged down in any individual concept. Students get a huge, wide exposure to content. This will help facilitate class conversations on day one, which is a real leverage. Holbrook introduces complex and technically challenging concepts in a way that, for those new to the world of R, is approachable and easy to understand. Fantastic introductory text for undergraduate study. Chapter 1 provides a strong overview of the research process. While it talks a lot about data, it does so in a non-technical way that I think most undergraduates would be able to get through reasonably well. The examples provided are relevant and broadly interesting. This text is structured well for taking students through Data Analysis for political science. Students are walked through both the meaning of the statistics examined and how the computer can be made to generate them. Each section builds on the preceding sections in a clear manner. The only problem with this book is that I wish I had written it. I can′t wait to use it. Professor Holbrook′s book provides an accessible entry-point for students of all levels to use data for political and social research, while offering clear and easy-to-understand guidance to the use of the R software. This is a highly engaging and practical introduction to social research using R. All the essentials are here with little to no distracting material that might confuse already anxious students. I highly recommend this text.